A Multiplex Real-Time PCR Assay Differentiates Four<i>Xanthomonas</i>Species Associated with Bacterial Spot of Tomato
Bibliographic record
Abstract
Bacterial spot of tomato, a major problem in many tomato production areas, is caused by Xanthomonas euvesicatoria, X. vesicatoria, X. perforans, and X. gardneri. In order to detect and identify the bacterial spot pathogens, we evaluated a region of hrpB operon as a source for primers and probes for real-time polymerase chain reaction (PCR). A 420-bp fragment of the hrpB7 gene was amplified by PCR from 75 strains representing the four species. The PCR products were sequenced and phylogenetic analysis revealed that hrpB7 is highly conserved within each species, with a single-nucleotide polymorphism (SNP) among the X. vesicatoria strains. X. euvesicatoria and X. perforans varied by two SNP. Four probes and two primer sets were designed to target the four bacterial spot pathogens based on their hrpB7 gene sequences. In order to simultaneously detect the four bacterial spot pathogens, the four probes and two primer sets were optimized for a multiplex real-time TaqMan PCR assay. The optimized multiplex assay was determined to be highly specific to the four bacterial spot pathogens. Because the optimized multiplex assay facilitated the identification of each bacterial spot pathogen from pure cultures and infected plant tissue, it holds great potential as a diagnostic tool.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".